Art of Analytics: Funding Fountains

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About the Insights

This anonymized visualization is one of a series of analytics mapping the money flows between large Chinese companies for a Corporate Banking Risk Analysis project at a large Chinese bank. The analysis uses Fund Transfer transaction data to understand risk and uncover market opportunities.


In this graph, the dots (nodes) represent the companies, via their account holdings. The lines (edges) represent a transfer of funds between the companies, so each line shows a movement of money from one account to another.


The charts shows all the money flows between the different colored companies. We can map flows through 2,3 and 4 subsequent transactions, such as the light green company, to understand upstream supply chains and the interdependency companies have on each other.


To manage risk, the bank can identify any large exposure concentrations to groups of highly interdependent companies, where a single failure may bring down all the companies. It allows the bank to identify the critical companies in the supply chains and independently cross check a company's cash flow to verify its health. It also helps identify fraud. The bank can check the true business activity of a company and can verify that loaned funds are used for their stated purpose. For example a manufacturer that is investing speculative funds in the stock market rather than paying suppliers or who took out a loan to build a factory but really used the funds for short-term residential real estate trades.


For marketing it highlights gaps in the banks servicing. Where high volumes of funds flow out (or in) to the chains identifies high value prospect companies. For existing clients it reveals any high value gaps in service provision for wider financial services such as financing, clearing and risk management.


About the Analytics

This analysis uses Teradata Aster and Aster Lens. The transaction data loaded was very large in size: 60,802,990 records for over 670,000 companies. The company records contain industry classification codes so we can understand their business activity. For this chart PageRank was used to select the top 32 important customers and we included all the relevant counterparties with total transactions greater than or equal to CNY 700,000. (USD 115k).


In this graph, there are 3883 nodes and 3943 edges. The nodes represent the companies while the edges represent the cash flows between the companies.


About the Analyst

Qiling (Mary) Shi is part of a pioneering group of Chinese Data Scientists that have been partnering with the banks in China to experiment with large-scale risk analytics using high intensity super graphic methods. Their goal is to uncover new ways of managing risk in Chinas highly complex commercial system. Their work on corporate customers, including 'Fund Fountains', is just one example of a series of innovations that this talented group have given to the wider banking world, to help de-risk our financial systems.


Qiling is a presale consultant for Teradata China's Aster & Hadoop Big Data Center Of Excellence (COE). Qiling got her PhD degree in Applied Mathematics at University of Central Florida. She is currently doing her MBA part-time at the University of Delaware. Prior to Teradata, she worked in the risk management department of PNC Bank in Pittsburgh for over 2 years. During that time, she developed many algorithms to fight fraud and money laundering; several of which were reported to the Office of Currency Controller by PNC. She has also developed and published computer programs in SAS conferences while working for the Computer Sciences Corporation.